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On the Exchange of Sensible and Latent Heat Between the Atmosphere and Melting Snow

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On the Exchange of Sensible and Latent Heat Between the Atmosphere and Melting Snow On the exchange of sensible and latent heat between the atmosphere and melting snow Authors: Paul C. Stoy, Erich Peitzsch, David Wood, Daniel Rottinghaus, Georg Wohlfahrt, Michael Goulden, and Helen Ward NOTICE: this is the author’s version of a work that was accepted for publication in Agricultural and Forest Meteorology. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Agricultural and Forest Meteorology, Volume 252, April 2018, DOI#10.1016/j.agrformet.2018.01.028 Stoy, Paul C. , Erich Peitzsch, David Wood, Daniel Rottinghaus, Georg Wohlfahrt, Michael Goulden, and Helen Ward. "On the exchange of sensible and latent heat between the atmosphere and melting snow." Agricultural and Forest Meteorology 252 (April 2018): 167-174. DOI:10.1016/j.agrformet.2018.01.028. Made available through Montana State University’s ScholarWorks scholarworks.montana.edu On the exchange of sensible and latent heat between the atmosphere and T melting snow ⁎ Paul C. Stoya, , Erich Peitzschb,c, David Wooda,c, Daniel Rottinghausa, Georg Wohlfahrtd, Michael Gouldene, Helen Wardf,g a Department of Land Resources and Environmental Sciences, Montana State University, Bozeman, MT, USA b Department of Earth Sciences, Montana State University, Bozeman, MT, 59717 USA c U.S. Geological Survey Northern Rocky Mountain Science Center, Bozeman, MT, 59717, USA d Institute of Ecology, University of Innsbruck, Sternwartestrasse 15, 6020 Innsbruck, Austria e Department of Earth System Science, University of California, Irvine, Irvine, CA, 92697, USA f Department of Meteorology, University of Reading, Reading, RG6 6BB, United Kingdom g Institute of Atmospheric and Cryospheric Sciences, University of Innsbruck, 6020 Innsbruck, Austria ABSTRACT The snow energy balance is difficult to measure during the snowmelt period, yet critical for predictions of water yield in regions characterized by snow cover. Robust simplifications of the snowmelt energy balance can aid our understanding of water resources in a changing climate. Research to date has demonstrated that the net tur- bulent flux (FT) between a melting snowpack and the atmosphere is negligible if the sum of atmospheric vapor pressure (ea) and temperature (Ta) equals a constant, but it is unclear how frequently this situation holds across different sites. Here, we quantified the contribution of FT to the snowpack energy balance during 59 snowmelt periods across 11 sites in the FLUXNET2015 database with a detailed analysis of snowmelt in subarctic tundra near Abisko, Sweden. At the Abisko site we investigated the frequency of occurrences during which sensible heat flux (H) and latent heat flux (λE) are of (approximately) equal but opposite sign, and if the sum of these terms, FT, is therefore negligible during the snowmelt period. H approximately equaled -λE for less than 50% of the melt period and FT was infrequently a trivial term in the snowmelt energy balance at Abisko. The reason is that the relationship between observed ea and Ta is roughly orthogonal to the “line of equality” at which H equals -λE as warmer Ta during the melt period usually resulted in greater ea. This relationship holds both within melt periods at individual sites and across different sites in the FLUXNET2015 database, where FT comprised less than 20% of the energy available to melt snow, Qm, in 44% of the snowmelt periods studied here. FT/Qm was significantly −2 related to the mean ea during the melt period, but not mean Ta, and FT tended to be near 0 W m when ea averaged ca. 0.5 kPa. FT may become an increasingly important term in the snowmelt energy balance across many global regions as warmer temperatures are projected to cause snow to melt more slowly and earlier in the year under conditions of lower net radiation (Rn). Eddy covariance research networks such as Ameriflux must improve their ability to observe cold-season processes to enhance our understanding of water resources and surface-atmosphere exchange in a changing climate. 1. Introduction Leathers, 1999; Hayashi et al., 2005; Pederson et al., 2013), and snowmelt is projected to occur earlier and more slowly in a warming Quantifying the snowpack energy balance is critical for water reg- climate under conditions of lower net radiation earlier in the season ulation and runoff prediction (Dettinger et al., 2015; Kay and Crooks, (Musselman et al., 2017). To understand how snowmelt responds to 2014; Marks et al., 2008; Troin et al., 2016), avalanche forecasting climate variability, we must understand mass and energy fluxes to and (Slaughter et al., 2009; Wever et al., 2016), and predicting changes to from the snowpack, including key relationships that can simplify future snowpack persistence (Abatzoglou et al., 2014; Pederson et al., models without impacting their skill. 2011). Interannual variability in weather and climate change impact Using the convention that energy flux into the snowpack is positive, the timing and magnitude of snowmelt (Cline, 1997; Grundstein and the energy available to melt snow, Qm, is a function of the net radiation − λ “ ” (Rn, i.e. incident minus outgoing shortwave and longwave radiation), which H = E, hereafter the line of equality , and derived below in sensible heat flux (H), latent heat flux (λE), ground heat flux (G), and Methods. It is unclear if other melting snowpacks experience similar fl any energy ux due to precipitation (P, Marks and Dozier, 1992; Burns average climate conditions that make FT negligible during the melt et al. 2014): period and therefore when Rn measurements alone provide an accurate approximation of Qm (Eq. (1), noting that the magnitude of G is often Qm + Qcc = Rn + H + λE + G + P = Rn + FT + G + P. (1) trivial compared to other terms in Eq. (1) during snowmelt. Here, we quantify the contribution of FT to Qm during two snowmelt Qcc, the energy required to bring snow temperature to melting temperature (often called the cold content), is assumed here to be 0 W periods at a subarctic tundra research site near Abisko, Sweden and 59 −2 snowmelt periods across 11 eddy covariance study sites in the m when the snowpack is melting. The net turbulent flux, FT, is the sum of H and λE, the latter being of particular interest to snow science FLUXNET2015 database (Pastorello et al., 2017) to quantify the range as it represents sublimation and evaporation from and condensation to of meteorological conditions encountered during the melt period in ff the snowpack, and is thus connected to the snow mass balance.Hand di erent snowpacks. We examined two questions. First, are the mi- crometeorological conditions during the snowmelt period observed in λE tend to be minor but nontrivial contributions to Qm (Boon, 2009; ff Cline, 1997; Harding and Pomeroy, 1996; Fitzpatrick et al. 2017; Marks Welch et al. (2016) common for various sites with di erent physical and Winstral, 2001) and are arguably more difficult to measure via, for characteristics? To address this question, we examined eddy covariance example, eddy covariance compared to the radiometers and heat flux and radiometric measurements of energy exchange between the snowpack and the atmosphere from sites in different climate zones. plates used to measure Rn and G (Arck and Scherer, 2002). Under- −2 Second, how frequently is FT approximately equal to 0 W m , and standing situations in which the contribution of FT to Qm is negligible what is the relative contribution of FT to Qm during the snowmelt period would dramatically simplify our ability to measure and model Qm. In a comparison of studies at alpine sites over portions of the melt across sites? To address this question we study the distribution of mi- crometeorological conditions during different melt events. The goal of period, Cline (1997) found that the contribution of Rn to Qm ranged this analysis is to gain a better understanding of conditions in which the from 0 to 100%, illustrating that FT can be both a negligible and snowmelt energy balance can be accurately approximated using dominant source of energy for snowmelt. FT can dominate Qm in arid radiometric observations to simplify measurements and models. We environments, especially in the early season before Rn reaches higher focus our discussion on the steps necessary to improve observations of values (Beaty, 1975; Hawkins and Ellis, 2007). The contribution of FT to cold season processes within surface-atmosphere flux networks like Qm can vary due to weather patterns (Cline, 1997; Grundstein and fl Leathers, 1999; Hayashi et al., 2005), wind speed (Mott et al., 2011; Ameri ux, and to improve observations of climate and surface-atmo- fl Pohl et al., 2006), and vegetation (Endrizzi and Marsh, 2010; Mahrt and sphere ux at snowmelt measurement networks like SNOTEL (snow- Vickers, 2005), which makes model simplification difficult. However, pack telemetry, Serreze et al., 1999). the time scales of these comparisons range from the entire snow cov- ered period to less than a week, and it is unclear how frequently, and 2. Methods under which conditions, FT contributes negligibly to Qm when snow is melting. 2.1. Snow energy balance and turbulent flux during snowmelt Welch et al. (2016) used eddy covariance measurements in a Welch et al. (2016) present a relationship in which the input of H to montane continental snowpack in Montana, USA, and found that H was − the snowpack is equal to −λE (i.e. F =0Wm 2) when snow is only about 10% less than the magnitude of -λE, such that FT T −2 −2 melting. Briefly, H can be written following e.g. Kaimal and Finnigan (−3MJm ) provided a negligible contribution to Qm (97 MJ m ) when integrated over the entire melt period.
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